Available online at www.sciencedirect.com ScienceDirect Procedia Technology 24 (2016) 873 – 879 International Conference on Emerging Trends in Engineering, Science and Technology (ICETEST - 2015) Achievable Data Rate in Hybrid MIMO Cognitive Radio Networks Satheesan Ua, Sudha Tb a M.Tech Student, Electronics and Communication Department, NSS College of Engineering, Palakkad, India b Professor, Electronics and Communication Department, NSS College of Engineering, Palakkad, India Abstract Cognitive radio plays a vital role in wireless communication Hybrid cognitive radio networks work in both underlay and overlay modes The data rate of such hybrid cognitive radio network is limited M IM O system can be implemented in hybrid cognitive radio networks for improving the data rate Beam forming is used over the M IM O antennas to reduce the interference level in the desired direction The game model called Nash Equilibrium (NE) is used for power control in secondary users (SUs) and primary users (PUs) Our investigations show that the achievable data rate increases due to the applied beam forming technique in M IM O antennas for hybrid cognitive radio network 2016The TheAuthors Authors.Published Elsevier © 2016 © Published byby Elsevier Ltd.Ltd This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the organizing committee of ICETEST – 2015 Peer-review under responsibility of the organizing committee of ICETEST – 2015 Keywords: Multiple Input Multiple Output (MIMO); Beam forming; Game theory ; Nash Equilibrium (NE); Utility; Achievable data rate Introduction With the rapid deployment of wirel ess servi ces over the last decad e, the radio spectrum is becoming a valuable and scarce resource How to support growing applications with limited spectrum resources emerges as a critical issue for future wirel ess communications On the other side, the report from the Federal Communications Commission rev eals that most of the licensed spectrum is severely underutilized As a promising technique, cognitive radio (CR) is proposed to deal with the dilemma between spect rum scarcity and spectrum under utilization CR allows unlicensed users [referred to as secondary users (SUs)] to acces s licensed bands under the condition that the induced interferen ce to the licensed users [referred to as primary users (PUs)] does not reach an unacceptabl e level [1]-[4] Hybrid cognitive radio network works in both overlay mode and underlay mode If the PU is detect ed to be active, the SU selects the spectrum underlay mode and transmits with lower power Otherwise, the SU works at spectrum Phone no: 9995392087 Satheesan91@gmail.com 2212-0173 © 2016 The Authors Published by Elsevier Ltd This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the organizing committee of ICETEST – 2015 doi:10.1016/j.protcy.2016.05.144 874 U Satheesan and T Sudha / Procedia Technology 24 (2016) 873 – 879 overlay and transmits with its maximum power budget for a higher dat a rat e So the throughput of the network is improved by using hybrid CR network [5]-[8] Recently, a new paradigm termed Cooperative Cognitive Radio Networks (CCRNs) has been introduced In CCRN, PUs may select some SUs to relay the primary traffi c cooperatively and in return grant portion of the ch annel access time to the SUs By exploiting cooperative diversity, the transmission rates o f PUs can be significantly improved But the data rate o f such CCRN can be furth er improved by implementing MIMO antennas in PUs and SUs In MIMO there are multiple antennas and they are used for simultaneous transmission as well as reception MIMO has the advantage due to multiple antennas and advan ced signal processing technique used By using this technique, multiple number of data streams can be transmitted or received over the MIMO antennas independ ently The interference introduced by the nearby ant ennas is the main problem of the MIMO technique In our work beam forming technique can be introduced in the MIMO antennas to reduce the interferen ce and to attain improved data rat e Beam forming is an alternative name for spatial filtering where, with appropriate an alog or digital signal processing, an array of antennas can be steered in a way to block the reception of radio signals coming from speci fi ed directions This can be achiev ed in such a way that signals at particular direction have constructive interference while others have destructive interference The primary user tries to maximize the transmission rate while secondary users compete with each other to access the channel [9]-[11] In hybrid cognitive radio networks power allocation is done by using power bidding and allocation algorithm [12] But this mechanism is complex in CCRN In our work game theory based on Nash Equilibrium concept is used for power control in PUs and SUs Game theory maximizes the utility of PU and SU [13] The rest of this paper is organized as follows In section II, system model is described Section III gives the power allocation using game theory Utility analysis is described in Section IV Simulation results are presented in Section V Section VI concludes the findings of the paper System Model In our system model, MIMO antennas are implemented in hybrid cognitive radio networks The system model consists of primary transmitter (PT), primary receiv er (PR) and secondary users (SUs) Consider SU and PU are equipped with two MIMO antennas Such a 2×2 MIMO system is shown in Fig.1 The number of SUs participating in cooperative communication is decided by PU and the sel ected S Us are called rel ays Fig.2 shows the structure o f MIMO-CR N In this model two stages are used In first stage the Primary User (PU) transmits signal to the secondary rel ays Then in the second stage the rel ays transmit the data to the primary receiver The SU helps the PU by acting as relays and in turn channel is provided to the relay s to transmit their own data [13] Fig.1 2×2 MIMO The received signal at the antenna is given by ܻ ൌ ܺܪ ܰ (1) where H represents the Channel gain, N denotes the AWGN (Additive White Gaussian Noise) and X is the information signal The MIMO transmission is explained in two stages U Satheesan and T Sudha / Procedia Technology 24 (2016) 873 – 879 Fig.2 Structure of MIMO CRN First stage: the primary user sel ects SU1 and SU3 as the rel ays for cooperation; SUs are generally denoted as R We use h 1r to represent the chann el coeƥci ent of primary signal X1 and HRr to represent the channel vector of secondary signal X2.The users apply precoding vectors; they are denoted as u for encod ing vector and v for decoding vector The received signal in the stage on each relay is a combination of primary and secondary signals (2) ܻ ൌ ݄ ଵ ܺଵ ܪோ ݑ௦ ܺଶ ݊ ሺʹ ሻ The each relay applies a decoding vector vp† to decode the primary signal by making HRrus X2 = and to obtain secondary data, the SU applies v s† to make v s† h 1r =0 The received secondary stream at the SU after decoding is ൌ ߥ ௦ற ܪோ ݑ௦ ܺଶ ߥ ௦ற ݊ (3) ܻ and decoded primary signal at the relay is denoted as Yp (4) ܻ ൌ ߥ ற ݄ଵ ܺଵ ߥ ற ݊ where u s and u p are used to denote the encoding vectors of secondary and primary signal respectively v s† and v p† are used to the decoding vectors of secondary signal and primary signal respectively Second stage: The chosen relays transmit the primary data to the primary receiver PR We use h r1 to represent the ch annel vector fo rm relay r to PR and HrR to represent the channel vector from rel ay r to SU At the PR secondary signal is nulled so HrRu s X2 = The signal received at PR is ൌ σ ݄ ଵ ܺଵ ξܲ ݑ ݊ (5) ܻ where, P r represents the power used for relays The values of P r are found using game theory Due to Maximum Ratio Combining, the eơective SNR at PR equals to the sum of all SNRs from all the secondary rel ays The transmission power of primary transmitter PT is denoted as P p , the data rat e of primary stream at selected relay is ଶ ൗ ேబ ܴܦ௦ ൌ ଶሺͳ ߥ ற ݄ ଵ ሻ (6) In the PR sum of SNR of all relays are done by MRC method, thus rate of primary signal at PR is given by ଶ ൗ ேబ ܴܦ௦ ൌ ଶሺͳ σ ݄ ଵ ݑሻ (7) For the secondary data rate, the transmission power of SUs given as P r, thus the resulting secondary rate is ଶ ೝ ൗ ேబ ܴܦ௦௦ ൌ ଶሺͳ ߥ ௦ற ்ܪ ݑ௦ ܵ௦ ሻ (8) Here the data rate o f secondary signal is less than primary due to self-interferen ce caused by large number of SUs and in MIMO-CCRN more importance is given to primary users compared to secondary users Power Allocation Using Game Theory In this system model data is transmitted in two stages Time division multiple access (TDMA) is used for dat a 875 876 U Satheesan and T Sudha / Procedia Technology 24 (2016) 873 – 879 transmission We are denoting the time duration as αT i for first stage and (1−α)T i for second stage and the time length as α We are denoting the secondary pair which are parti cipating in stage as Q1 and the pair in stage as Q2 Since in CCRN the secondary users are followers of primary user for cooperation Hen ce all the users are selfish aiming to maximize the utilities In order to find the best P r, the Nash Equilibrium is used to find primary users utility and P k is the secondary users power, here k denotes secondary users [13] The utility of each secondary pair is the di ơerence bet ween the dat a rat e (DRi ) and the cost of the power The utility of secondary pair is denoted as M (9) ܯଵ ൌ ܶ ሺܴܦ െ ߱ܲ ሻ െ ߱ܲ ሺͳ െ ߙሻ ܶ where ω represent the cost for a unit transmission The energy consumed by the relay of secondary users are denoted as ܶ ൌ ܨ σ ೖ (10) ೕ אೂ ೕ The secondary users in each pair are play ers For non cooperative power allocation game, the strategy is achiev ed by Nash Equilibrium for each relay The utility of the secondary user in Q1 is ೖ ܯଵ ൌ ߙܶ σ ܴܦ௦௦ െ ߱ܲ ሺͳ െ ߙሻ ܶ ೝ (11) In this Nash Equilibrium (NE) is analyzed for secondary pairs in Q1 By using similar method Q2 can be analyzed Now to solve the power for secondary pairs with unique Nash equilibrium for the first stage Q is ܲ כൌ where ߙ ൌ ఈ ߙ ሺ ଵିఈ ሻ (12) ሺȁொ భ ିଵȁሻ ఠ σ אೂ భ ವೃೞೞ ሺͳ െ ሺȁொ భିଵȁሻ ோ ೞೞ σ אೂ భ ವೃೞೞ (13) ܲ כrepresent the power for each relay in Q1 Similarly using NE the secondary power among the relays in Q2 is b k as ሺȁொ ିଵሻȁሻ ሺȁொ ିଵሻȁሻ (14) ܾ ൌ σ మ భ ሺͳ െ ೞೞ σమ భ ఠ אೂ ವೃೞೞ ோ אೂ ವೃೞೞ Utility Analysis for Primary User The resulting data rate for PU is ܣൌ σ ܳ אଵ ȁೝభ ௨ ȁమೖ ேబ (15) and ܤൌ σ ܳ אଶ ȁೝభ ௨ ȁమೖ ேబ Thus the resulting data rate in stage is ఈ ௦ ሻ ܴܦ ൌ ଶሺͳ ܣǤ ଵିఈ ାೖ DR i ps can ps (16) (17) Similarly rate fo r be obtained In order to maximize the utility, the data rat e of st age1 T i stage T i k אQ2 DRi is maintained equally Therefore ௦ ௦ (18) ܶ אொ భ ܴܦ ൌ ܶ אொ మܴܦ k אQ1 DR i sp and Simulation Results Fig and Fig show the primary users utility in stage and stage as a function of k, where k is the number of rel ays The unit of utility is denoted in Mbps As the number of relays increases, more number of secondary users U Satheesan and T Sudha / Procedia Technology 24 (2016) 873 – 879 877 acts as relays to transmit PUs data So the data o f primary transmitter reach es at primary receiver with a high signal to noise ratio (SNR) This will increase the PUs utility with k for stage and stage Fig and Fig shows the SUs utility as a function of number of rel ays fo r stag e and stage As the number of rel ays increases, SUs are helping PUs by acting as relays to transmit primary users data So SUs utility decreases with increase in number of relays Fig illustrates the perform ance of 2x2 hybrid MIMO cognitive radio networks without using beam forming technique The PU and SU data rate is plotted for various SNR The PU data rat e is high compared to SU Fig illustrates the perform ance of 2x2 hybrid MIMO cognitive radio networks using beam forming technique By introducing beam forming technique in hybrid MIMO cognitive radio networks, interferen ce gets reduced So the achiev able dat a rate o f PU and SU increases From the Fig and Fig we can infer that the data rate o f 2x2 hybrid MIMO system with beam forming technique is higher than the data rat e of 2x2 hybrid MIMO system without beam forming technique Fig.3 PU Utility in stage Fig.4 PU Utility in stage 878 U Satheesan and T Sudha / Procedia Technology 24 (2016) 873 – 879 Fig.5 SU Utility in stage Fig.6 SU Utility in stage Fig.7 Data Rate without Beam forming U Satheesan and T Sudha / Procedia Technology 24 (2016) 873 – 879 Fig.8 Data Rate with Beam forming Conclusion In this paper, we have implemented MIMO technique in hybrid cognitive radio networks Beam forming technique is introduced into the MIMO antennas for redu cing the interference level in the desired direction The results of the investigation show that the data rate o f both PU and SU increases with the implementatio n of beam fo rming technique in MIMO antennas Game theory based on Nash Equillibrium is used for power control in PUs and SUs Acknowledgment This work is supported by AICTE, Govt of India through the project grant under Research promotion Scheme No.20/AICTE/RIFD/RPS (POLICY- 1)59/2013-14 References [1] Federal Communications Commission, “Spectrum Policy Task Force,” Report ET Docket no 02-135, Nov 2002 [2] K C Chen, Y J Peng, N Prasad, Y C Liang, and S Sun, “Cognitive Radio Network Architecture: Part I - General Structure,” ACM International Conference on Ubiquitous Information Management and Communication (ICUIMC), 2008 [3] J Mitola III and Gerald Q M., “Cognitive Radio: Making Software Defined Radios More Personal,”IEEE Personal Communications, Aug 1999 [4] Shrikrishan Yadav, Santosh Kumar Singh, Krishna Chandra Roy , “A Smart and Secure Wireless Communication Sy stem: Cognitive Radio,” International Journal of Soft Computing and Engineering (IJSCE) ISSN, Vol.2, Issue-1, pp.2231–2307, 2012 [5] Yahia Tachwali, Fadi 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forming technique is introduced into the MIMO antennas for redu cing the interference level in the desired direction The results of the investigation... dat a rate o f PU and SU increases From the Fig and Fig we can infer that the data rate o f 2x2 hybrid MIMO system with beam forming technique is higher than the data rat e of 2x2 hybrid MIMO